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An iterative run through a practical application might be:
• Obtain the mean for unique known features (e.g. the blue channel in
buildings).
• Subtract these known features from the area polygon.
• Obtain the mean values for the remaining pixels.
• Compare these values relative to the known features and determine the
most probable land coverage.
Following this initial sampling of roof values, it emerged that the values were not
consistent enough for reference to determine adjacent land use types (though the
fact that the polygons are coded vector polygons means they can be excluded form
pixel value searches of small area polygons. One aspect of the pixel values which
did emerge, and has potential for further analysis, was the clear division and pixel
count peaks for roofs with a pitch. Taken further, these values, in terms of their
relative pitch and matched to the set of proportional values that this study sets out
to establish, the ability to determine roof pitch could be included. This would
necessitate a separate study (over a large quantity of buildings across a wide
geographical area and will not form part of this study; although the algorithm
outlined in the thesis would be an extremely beneficial for such a study.
The sampling for roof values in proportional reference to adjacent areas took three
samples of separate land cover types and the road polygons adjacent to them. The
three areas analyzed were; a section of pasture close to a farm dwelling, a section
of mixed forest close to another dwelling (with several pitches in the roof) and a
section of cut pasture close to a flat roofed shed. As mentioned above the
sampling did not produce any useful data for the algorithm (except to eliminate
the possibility of buildings being used for spectral reference). It did, however,
outline the potential for a separate building analysis based on roof pitches. The
following few paragraphs outline the results of the comparison.
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